Vowel Acoustics as Predictors of Speech Intelligibility in Dysarthria

This is the code for the statistical analysis for “Vowel Acoustics as Predictors of Speech Intelligibility in Dysarthria.”

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Upload Datasets

Reliability <- rio::import("Prepped Data/Reliability Data.csv")
AcousticData <- rio::import("Prepped Data/AcousticMeasures.csv") %>%
  dplyr::mutate(intDiff = VAS - transAcc)

AcousticData <- AcousticData %>%
  dplyr::filter(!grepl("_rel", Speaker)) %>%
  dplyr::select(c(Speaker, Sex, Etiology, vowel_ED_b, VSA_b, autoVSA,
                  Hull_b,Hull_bVSD_25, Hull_bVSD_50, Hull_bVSD_75,
                  VAS, transAcc)) %>%
  dplyr::mutate(Etiology = as.factor(Etiology),
                Sex = as.factor(Sex),
                Speaker = as.factor(Speaker))

Listeners <- rio::import("Prepped Data/Listener_Demographics.csv") %>%
  dplyr::select(!c(StartDate:proloficID, Q2.4_6_TEXT, Q3.2_8_TEXT, AudioCheck:EP3))

Listeners$race[Listeners$Q3.3_7_TEXT == "Native American/ African amercing"] <- "Biracial or Multiracial"

Inter-rater Reliability

Two raters (the first two authors) completed vowel segmentation for the speakers. To calculate inter-rater reliability, 20% of the speakers were segmented again by the other rater. Two-way intraclass coefficients were computed for the extracted F1 and F2 from the temporal midpoint of the vowel segments. Since only one set of ratings will be used in the data analysis, we focus on the single ICC results and interpretation. However, we also report the average ICC values to be comprehensive.

## Creating new data frames to calculate ICC for extracted F1 and F2 values

F1_Rel <- Reliability %>%
  dplyr::select(c(F1, F1_rel))

F2_Rel <- Reliability %>%
  dplyr::select(c(F2, F2_rel))
  
## Single ICC for F1
Single_F1 <- irr::icc(F1_Rel, model = "twoway", type = "agreement", unit = "single")

## Average ICC for F1
Average_F1 <- irr::icc(F1_Rel, model = "twoway", type = "agreement", unit = "average")

## Single ICC for F2
Single_F2 <- irr::icc(F2_Rel, model = "twoway", type = "agreement", unit = "single")

## Average ICC for F2
Average_F2 <- irr::icc(F2_Rel, model = "twoway", type = "agreement", unit = "average")

## Inter-rater reliability results and interpretation

print(paste("Single ICC for F1 is ", round(Single_F1$value, digits = 3), ". ", 
            "The 95% CI is [", round(Single_F1$lbound, digits = 3), " - ", round(Single_F1$ubound, digits = 3), "].", sep = ""))

[1] "Single ICC for F1 is 0.866. The 95% CI is [0.837 - 0.89]."

print(paste("Single ICC for F2 is ", round(Single_F2$value, digits = 3), ". ", 
            "The 95% CI is [", round(Single_F2$lbound, digits = 3), " - ", round(Single_F2$ubound, digits = 3), "].", sep = ""))

[1] "Single ICC for F2 is 0.931. The 95% CI is [0.916 - 0.944]."

print(paste("Average ICC for F1 is ", round(Average_F1$value, digits = 3), ". ", 
            "The 95% CI is [", round(Average_F1$lbound, digits = 3), " - ", round(Average_F1$ubound, digits = 3), "].", sep = ""))

[1] "Average ICC for F1 is 0.928. The 95% CI is [0.911 - 0.942]."

print(paste("Average ICC for F2 is ", round(Average_F2$value, digits = 3), ". ", 
            "The 95% CI is [", round(Average_F2$lbound, digits = 3), " - ", round(Average_F2$ubound, digits = 3), "].", sep = ""))

[1] "Average ICC for F2 is 0.964. The 95% CI is [0.956 - 0.971]."

print("Thus, interrater reliability for the extracted F1 and F2 values from the vowel segments was good to excellent.")

[1] "Thus, interrater reliability for the extracted F1 and F2 values from the vowel segments was good to excellent."

## Removing extra data frames from environment

rm(F1_Rel, F2_Rel, Reliability, Single_F1, Single_F2, Average_F1, Average_F2)

Descriptive Statistics

Correlations

CorrMatrix <- AcousticData %>%
  dplyr::select(VSA_b, vowel_ED_b, Hull_b, Hull_bVSD_25, Hull_bVSD_75, VAS, transAcc) %>%
  as.matrix() %>%
  Hmisc::rcorr()

CorrMatrixP <- CorrMatrix$P < .05

CorrMatrix <- CorrMatrix$r

stats::cor.test(AcousticData$VSA_b, AcousticData$vowel_ED_b, method = "pearson")

    Pearson's product-moment correlation

data:  AcousticData$VSA_b and AcousticData$vowel_ED_b
t = 6.5285, df = 38, p-value = 1.076e-07
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 0.5372669 0.8468014
sample estimates:
      cor 
0.7270881 

stats::cor.test(AcousticData$Hull_b, AcousticData$Hull_bVSD_25, method = "pearson")

    Pearson's product-moment correlation

data:  AcousticData$Hull_b and AcousticData$Hull_bVSD_25
t = 9.4906, df = 38, p-value = 1.43e-11
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 0.7135136 0.9119080
sample estimates:
     cor 
0.838625 

write.csv(CorrMatrix, file = "Tables/Correlation Matrix.csv")
rm(CorrMatrix)

Research Q1: Modeling Intelligibility

Orthographic Transcriptions

Model 1

# Specifying Model 1

OT_Model1 <- lm(transAcc ~ Hull_bVSD_25, data = AcousticData)

## Model 1 Assumptions 

performance::check_model(OT_Model1)

## Model 1 Summary

summary(OT_Model1)

Call:
lm(formula = transAcc ~ Hull_bVSD_25, data = AcousticData)

Residuals:
    Min      1Q  Median      3Q     Max 
-47.896 -14.090   5.996  17.470  36.105 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept)   48.2973     9.7372   4.960  1.5e-05 ***
Hull_bVSD_25   0.6417     0.5663   1.133    0.264    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 23.83 on 38 degrees of freedom
Multiple R-squared:  0.03268,   Adjusted R-squared:  0.007224 
F-statistic: 1.284 on 1 and 38 DF,  p-value: 0.2643

Model 2

## Specifying Model 2

OT_Model2 <- lm(transAcc ~ Hull_bVSD_25 + Hull_bVSD_75, data = AcousticData)

## Model 2 Assumption Check

performance::check_model(OT_Model2)

## Model 2 Summary

summary(OT_Model2)

Call:
lm(formula = transAcc ~ Hull_bVSD_25 + Hull_bVSD_75, data = AcousticData)

Residuals:
    Min      1Q  Median      3Q     Max 
-48.854 -13.962   5.567  16.758  36.257 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept)   47.3374    10.3316   4.582 5.09e-05 ***
Hull_bVSD_25   0.8045     0.7781   1.034    0.308    
Hull_bVSD_75  -0.7188     2.3225  -0.309    0.759    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 24.11 on 37 degrees of freedom
Multiple R-squared:  0.03518,   Adjusted R-squared:  -0.01698 
F-statistic: 0.6745 on 2 and 37 DF,  p-value: 0.5156

## Model 1 and Model 2 Comparison

anova(OT_Model1, OT_Model2)

Analysis of Variance Table

Model 1: transAcc ~ Hull_bVSD_25
Model 2: transAcc ~ Hull_bVSD_25 + Hull_bVSD_75
  Res.Df   RSS Df Sum of Sq      F Pr(>F)
1     38 21570                           
2     37 21514  1    55.696 0.0958 0.7587

Model 3a

## Specifying Model 3

OT_Model3a <- lm(transAcc ~ Hull_bVSD_25 + Hull_bVSD_75 + Hull_b, data = AcousticData)

## Model 3 Assumption Check

performance::check_model(OT_Model3a)

performance::check_collinearity(OT_Model3a)

# Check for Multicollinearity

Low Correlation

         Term  VIF Increased SE Tolerance
 Hull_bVSD_75 2.00         1.41      0.50
       Hull_b 3.65         1.91      0.27

Moderate Correlation

         Term  VIF Increased SE Tolerance
 Hull_bVSD_25 5.33         2.31      0.19

## Model 3 Summary

summary(OT_Model3a)

Call:
lm(formula = transAcc ~ Hull_bVSD_25 + Hull_bVSD_75 + Hull_b, 
    data = AcousticData)

Residuals:
    Min      1Q  Median      3Q     Max 
-56.706 -13.157   7.018  17.957  29.990 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)  
(Intercept)   31.0806    14.4916   2.145   0.0388 *
Hull_bVSD_25  -0.8439     1.2984  -0.650   0.5199  
Hull_bVSD_75   0.3159     2.3714   0.133   0.8948  
Hull_b         1.2941     0.8247   1.569   0.1253  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 23.65 on 36 degrees of freedom
Multiple R-squared:  0.09695,   Adjusted R-squared:  0.0217 
F-statistic: 1.288 on 3 and 36 DF,  p-value: 0.2932

## Model 2 and Model 3 Comparison

anova(OT_Model2, OT_Model3a)

Analysis of Variance Table

Model 1: transAcc ~ Hull_bVSD_25 + Hull_bVSD_75
Model 2: transAcc ~ Hull_bVSD_25 + Hull_bVSD_75 + Hull_b
  Res.Df   RSS Df Sum of Sq      F Pr(>F)
1     37 21514                           
2     36 20137  1    1377.5 2.4626 0.1253

Model 3b

## Specifying Model 3

OT_Model3b <- lm(transAcc ~ Hull_bVSD_75 + Hull_b, data = AcousticData)

## Model 3 Assumption Check

performance::check_model(OT_Model3b)

performance::check_collinearity(OT_Model3b)

# Check for Multicollinearity

Low Correlation

         Term  VIF Increased SE Tolerance
 Hull_bVSD_75 1.26         1.12      0.79
       Hull_b 1.26         1.12      0.79

## Model 3 Summary

summary(OT_Model3b)

Call:
lm(formula = transAcc ~ Hull_bVSD_75 + Hull_b, data = AcousticData)

Residuals:
    Min      1Q  Median      3Q     Max 
-54.371 -12.860   5.038  17.725  31.609 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)  
(Intercept)   33.2337    13.9973   2.374   0.0229 *
Hull_bVSD_75  -0.6193     1.8702  -0.331   0.7424  
Hull_b         0.8605     0.4809   1.789   0.0818 .
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 23.47 on 37 degrees of freedom
Multiple R-squared:  0.08635,   Adjusted R-squared:  0.03697 
F-statistic: 1.749 on 2 and 37 DF,  p-value: 0.1881

Model 4

## Specifying Model 4

OT_Model4 <- lm(transAcc ~ Hull_bVSD_75 + Hull_b + VSA_b, data = AcousticData)

## Model 4 Assumption Check

performance::check_model(OT_Model4)

performance::check_collinearity(OT_Model4)

# Check for Multicollinearity

Low Correlation

         Term  VIF Increased SE Tolerance
 Hull_bVSD_75 1.26         1.12      0.79
       Hull_b 1.68         1.30      0.60
        VSA_b 1.45         1.20      0.69

## Model 4 Summary

summary(OT_Model4)

Call:
lm(formula = transAcc ~ Hull_bVSD_75 + Hull_b + VSA_b, data = AcousticData)

Residuals:
    Min      1Q  Median      3Q     Max 
-47.705 -12.573   3.108  14.504  35.215 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)   
(Intercept)   30.7726    12.7686   2.410  0.02119 * 
Hull_bVSD_75  -0.8216     1.7037  -0.482  0.63256   
Hull_b         0.1202     0.5049   0.238  0.81322   
VSA_b          5.8426     1.9859   2.942  0.00567 **
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 21.36 on 36 degrees of freedom
Multiple R-squared:  0.2634,    Adjusted R-squared:  0.2021 
F-statistic: 4.292 on 3 and 36 DF,  p-value: 0.01092

## Model 3 and Model 4 Comparison

anova(OT_Model3b, OT_Model4)

Analysis of Variance Table

Model 1: transAcc ~ Hull_bVSD_75 + Hull_b
Model 2: transAcc ~ Hull_bVSD_75 + Hull_b + VSA_b
  Res.Df   RSS Df Sum of Sq      F   Pr(>F)   
1     37 20373                                
2     36 16424  1    3948.9 8.6555 0.005673 **
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Model 5

## Specifying Model 5

OT_Model5 <- lm(transAcc ~ Hull_bVSD_75 + VSA_b + vowel_ED_b, data = AcousticData)

## Model 4 Assumption Check

performance::check_model(OT_Model5)

performance::check_collinearity(OT_Model5)

# Check for Multicollinearity

Low Correlation

         Term  VIF Increased SE Tolerance
 Hull_bVSD_75 1.10         1.05      0.91
        VSA_b 2.30         1.52      0.43
   vowel_ED_b 2.15         1.47      0.46

## Model 4 Summary

summary(OT_Model5)

Call:
lm(formula = transAcc ~ Hull_bVSD_75 + VSA_b + vowel_ED_b, data = AcousticData)

Residuals:
    Min      1Q  Median      3Q     Max 
-46.501 -11.965   2.561  14.356  33.849 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)  
(Intercept)   24.4273    20.1254   1.214   0.2327  
Hull_bVSD_75  -0.5814     1.5872  -0.366   0.7163  
VSA_b          5.2219     2.4990   2.090   0.0438 *
vowel_ED_b     6.0028    12.7240   0.472   0.6399  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 21.31 on 36 degrees of freedom
Multiple R-squared:  0.2668,    Adjusted R-squared:  0.2057 
F-statistic: 4.367 on 3 and 36 DF,  p-value: 0.0101

## Model 3 and Model 4 Comparison

anova(OT_Model4, OT_Model5)

Analysis of Variance Table

Model 1: transAcc ~ Hull_bVSD_75 + Hull_b + VSA_b
Model 2: transAcc ~ Hull_bVSD_75 + VSA_b + vowel_ED_b
  Res.Df   RSS Df Sum of Sq F Pr(>F)
1     36 16424                      
2     36 16349  0    75.229         

Final Model

## Specifying Final Model

OT_Model_final <- lm(transAcc ~ VSA_b, data = AcousticData)

## Final Model Assumption Check

performance::check_model(OT_Model_final)

## Final Model Summary

summary(OT_Model_final)

Call:
lm(formula = transAcc ~ VSA_b, data = AcousticData)

Residuals:
   Min     1Q Median     3Q    Max 
-46.72 -12.69   2.97  14.37  35.39 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept)   32.508      7.857   4.138 0.000187 ***
VSA_b          5.872      1.613   3.641 0.000807 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 20.86 on 38 degrees of freedom
Multiple R-squared:  0.2586,    Adjusted R-squared:  0.2391 
F-statistic: 13.25 on 1 and 38 DF,  p-value: 0.0008068

confint(OT_Model_final)

                2.5 %    97.5 %
(Intercept) 16.602765 48.413532
VSA_b        2.606927  9.137097

VAS Models

Model 1

# Specifying Model 1

VAS_Model1 <- lm(VAS ~ Hull_bVSD_25, data = AcousticData)

## Model 1 Assumptions 

performance::check_model(VAS_Model1)

## Model 1 Summary

summary(VAS_Model1)

Call:
lm(formula = VAS ~ Hull_bVSD_25, data = AcousticData)

Residuals:
    Min      1Q  Median      3Q     Max 
-47.625 -16.684   8.462  19.440  37.352 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept)   42.6328    10.7512   3.965 0.000313 ***
Hull_bVSD_25   0.5877     0.6253   0.940 0.353236    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 26.31 on 38 degrees of freedom
Multiple R-squared:  0.02272,   Adjusted R-squared:  -0.003001 
F-statistic: 0.8833 on 1 and 38 DF,  p-value: 0.3532

Model 2

## Specifying Model 2

VAS_Model2 <- lm(VAS ~ Hull_bVSD_25 + Hull_bVSD_75, data = AcousticData)

## Model 2 Assumption Check

performance::check_model(VAS_Model2)

## Model 2 Summary

summary(VAS_Model2)

Call:
lm(formula = VAS ~ Hull_bVSD_25 + Hull_bVSD_75, data = AcousticData)

Residuals:
    Min      1Q  Median      3Q     Max 
-47.850 -16.576   8.382  19.448  37.237 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept)   42.3384    11.4211   3.707 0.000684 ***
Hull_bVSD_25   0.6376     0.8602   0.741 0.463195    
Hull_bVSD_75  -0.2204     2.5674  -0.086 0.932036    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 26.66 on 37 degrees of freedom
Multiple R-squared:  0.02291,   Adjusted R-squared:  -0.0299 
F-statistic: 0.4338 on 2 and 37 DF,  p-value: 0.6513

## Model 1 and Model 2 Comparison

anova(VAS_Model1, VAS_Model2)

Analysis of Variance Table

Model 1: VAS ~ Hull_bVSD_25
Model 2: VAS ~ Hull_bVSD_25 + Hull_bVSD_75
  Res.Df   RSS Df Sum of Sq      F Pr(>F)
1     38 26296                           
2     37 26291  1     5.239 0.0074  0.932

Model 3a

## Specifying Model 3

VAS_Model3a <- lm(VAS ~ Hull_bVSD_25 + Hull_bVSD_75 + Hull_b, data = AcousticData)

## Model 3 Assumption Check

performance::check_model(VAS_Model3a)

performance::check_collinearity(VAS_Model3a)

# Check for Multicollinearity

Low Correlation

         Term  VIF Increased SE Tolerance
 Hull_bVSD_75 2.00         1.41      0.50
       Hull_b 3.65         1.91      0.27

Moderate Correlation

         Term  VIF Increased SE Tolerance
 Hull_bVSD_25 5.33         2.31      0.19

## Model 3 Summary

summary(VAS_Model3a)

Call:
lm(formula = VAS ~ Hull_bVSD_25 + Hull_bVSD_75 + Hull_b, data = AcousticData)

Residuals:
    Min      1Q  Median      3Q     Max 
-56.444 -18.989   6.121  18.036  32.281 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)
(Intercept)   25.0400    16.0599   1.559    0.128
Hull_bVSD_25  -1.1164     1.4389  -0.776    0.443
Hull_bVSD_75   0.8805     2.6280   0.335    0.740
Hull_b         1.3770     0.9139   1.507    0.141

Residual standard error: 26.21 on 36 degrees of freedom
Multiple R-squared:  0.08087,   Adjusted R-squared:  0.00428 
F-statistic: 1.056 on 3 and 36 DF,  p-value: 0.3799

## Model 2 and Model 3 Comparison

anova(VAS_Model2, VAS_Model3a)

Analysis of Variance Table

Model 1: VAS ~ Hull_bVSD_25 + Hull_bVSD_75
Model 2: VAS ~ Hull_bVSD_25 + Hull_bVSD_75 + Hull_b
  Res.Df   RSS Df Sum of Sq      F Pr(>F)
1     37 26291                           
2     36 24731  1    1559.6 2.2702 0.1406

Model 3b

This model removes VSD25 because of it’s high VIF value > 5.

## Specifying Model 3b

VAS_Model3b <- lm(VAS ~ Hull_bVSD_75 + Hull_b, data = AcousticData)

## Model 3 Assumption Check

performance::check_model(VAS_Model3b)

performance::check_collinearity(VAS_Model3b)

# Check for Multicollinearity

Low Correlation

         Term  VIF Increased SE Tolerance
 Hull_bVSD_75 1.26         1.12      0.79
       Hull_b 1.26         1.12      0.79

## Model 3 Summary

summary(VAS_Model3b)

Call:
lm(formula = VAS ~ Hull_bVSD_75 + Hull_b, data = AcousticData)

Residuals:
    Min      1Q  Median      3Q     Max 
-53.356 -19.394   8.036  21.298  33.412 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)  
(Intercept)   27.8884    15.5503   1.793   0.0811 .
Hull_bVSD_75  -0.3567     2.0777  -0.172   0.8646  
Hull_b         0.8034     0.5343   1.504   0.1412  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 26.07 on 37 degrees of freedom
Multiple R-squared:  0.0655,    Adjusted R-squared:  0.01499 
F-statistic: 1.297 on 2 and 37 DF,  p-value: 0.2855

Model 4

## Specifying Model 4

VAS_Model4 <- lm(VAS ~ Hull_bVSD_75 + Hull_b + VSA_b, data = AcousticData)

## Model 4 Assumption Check

performance::check_model(VAS_Model4)

performance::check_collinearity(VAS_Model4)

# Check for Multicollinearity

Low Correlation

         Term  VIF Increased SE Tolerance
 Hull_bVSD_75 1.26         1.12      0.79
       Hull_b 1.68         1.30      0.60
        VSA_b 1.45         1.20      0.69

## Model 4 Summary

summary(VAS_Model4)

Call:
lm(formula = VAS ~ Hull_bVSD_75 + Hull_b + VSA_b, data = AcousticData)

Residuals:
   Min     1Q Median     3Q    Max 
-45.56 -15.17   6.40  16.64  42.62 

Coefficients:
               Estimate Std. Error t value Pr(>|t|)   
(Intercept)   2.522e+01  1.426e+01   1.768  0.08553 . 
Hull_bVSD_75 -5.762e-01  1.903e+00  -0.303  0.76382   
Hull_b        5.056e-05  5.640e-01   0.000  0.99993   
VSA_b         6.340e+00  2.218e+00   2.858  0.00705 **
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 23.86 on 36 degrees of freedom
Multiple R-squared:  0.2383,    Adjusted R-squared:  0.1748 
F-statistic: 3.754 on 3 and 36 DF,  p-value: 0.01916

## Model 3 and Model 4 Comparison

anova(VAS_Model3b, VAS_Model4)

Analysis of Variance Table

Model 1: VAS ~ Hull_bVSD_75 + Hull_b
Model 2: VAS ~ Hull_bVSD_75 + Hull_b + VSA_b
  Res.Df   RSS Df Sum of Sq      F   Pr(>F)   
1     37 25145                                
2     36 20495  1    4649.8 8.1674 0.007046 **
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Model 5

## Specifying Model 5

VAS_Model5 <- lm(VAS ~ Hull_bVSD_75 + Hull_b + VSA_b + vowel_ED_b, data = AcousticData)

## Model 5 Assumption Check

performance::check_model(VAS_Model5)

## Model 5 Summary

summary(VAS_Model5)

Call:
lm(formula = VAS ~ Hull_bVSD_75 + Hull_b + VSA_b + vowel_ED_b, 
    data = AcousticData)

Residuals:
    Min      1Q  Median      3Q     Max 
-44.407 -13.917   7.397  16.408  41.322 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)  
(Intercept)  16.61280   23.81590   0.698    0.490  
Hull_bVSD_75 -0.40875    1.95955  -0.209    0.836  
Hull_b       -0.05465    0.58294  -0.094    0.926  
VSA_b         5.49348    2.91674   1.883    0.068 .
vowel_ED_b    6.68540   14.72389   0.454    0.653  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 24.13 on 35 degrees of freedom
Multiple R-squared:  0.2428,    Adjusted R-squared:  0.1562 
F-statistic: 2.805 on 4 and 35 DF,  p-value: 0.04041

## Model 4 and Model 5 Comparison

anova(VAS_Model4, VAS_Model5)

Analysis of Variance Table

Model 1: VAS ~ Hull_bVSD_75 + Hull_b + VSA_b
Model 2: VAS ~ Hull_bVSD_75 + Hull_b + VSA_b + vowel_ED_b
  Res.Df   RSS Df Sum of Sq      F Pr(>F)
1     36 20495                           
2     35 20375  1    120.02 0.2062 0.6526

Final Model

## Specifying Final Model

VAS_Model_final <- lm(VAS ~ VSA_b, data = AcousticData)

## Final Model Assumption Check

performance::check_model(VAS_Model_final)

## Final Model Summary

summary(VAS_Model_final)

Call:
lm(formula = VAS ~ VSA_b, data = AcousticData)

Residuals:
    Min      1Q  Median      3Q     Max 
-44.956 -15.943   6.754  17.153  43.062 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)   
(Intercept)   24.703      8.761   2.820  0.00760 **
VSA_b          6.163      1.798   3.427  0.00148 **
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 23.26 on 38 degrees of freedom
Multiple R-squared:  0.2361,    Adjusted R-squared:  0.216 
F-statistic: 11.74 on 1 and 38 DF,  p-value: 0.001482

confint(VAS_Model_final)

               2.5 %    97.5 %
(Intercept) 6.966936 42.438084
VSA_b       2.521887  9.803467

Research Q2: Relationship between OT and VAS

Model 1

# Specify Model

OT_VAS_model <- lm(transAcc ~ VAS*Etiology + VAS*Sex, data = AcousticData)

# Assumption Check

performance::check_model(OT_VAS_model)

# Model Results

summary(OT_VAS_model)

Final Linear Model

# Specify Final Model

OT_VAS_final <- lm(transAcc ~ VAS, data = AcousticData)

confint(OT_VAS_final)

# Model Results

summary(OT_VAS_final)

Corner Dispersion

Looking at corner dispersion as the sole predictor.

# Specify Final Model

OT_cornDisp <- lm(transAcc ~ vowel_ED_b, data = AcousticData)
summary(OT_cornDisp)

Call:
lm(formula = transAcc ~ vowel_ED_b, data = AcousticData)

Residuals:
    Min      1Q  Median      3Q     Max 
-53.949 -10.348   4.268  14.982  25.903 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)   
(Intercept)    6.227     18.611   0.335  0.73977   
vowel_ED_b    25.564      8.946   2.857  0.00689 **
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 21.98 on 38 degrees of freedom
Multiple R-squared:  0.1769,    Adjusted R-squared:  0.1552 
F-statistic: 8.165 on 1 and 38 DF,  p-value: 0.006891

VAS_cornDisp <- lm(VAS ~ vowel_ED_b, data = AcousticData)
summary(VAS_cornDisp)

Call:
lm(formula = VAS ~ vowel_ED_b, data = AcousticData)

Residuals:
    Min      1Q  Median      3Q     Max 
-52.146 -13.413   7.142  18.719  32.964 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)  
(Intercept)   -2.859     20.636  -0.139   0.8905  
vowel_ED_b    26.819      9.920   2.704   0.0102 *
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 24.37 on 38 degrees of freedom
Multiple R-squared:  0.1613,    Adjusted R-squared:  0.1393 
F-statistic:  7.31 on 1 and 38 DF,  p-value: 0.0102

Manuscript Tables

Descriptives Table

gtData <- AcousticData %>%
  rbind(.,AcousticData %>%
          dplyr::mutate(Etiology = "All Etiologies")) %>%
  rbind(.,AcousticData %>%
          rbind(.,AcousticData %>%
          dplyr::mutate(Etiology = "All Etiologies")) %>%
          dplyr::mutate(Sex = "All")) %>%
  dplyr::mutate(Sex = as.factor(Sex),
                Etiology = as.factor(Etiology)) %>%
  dplyr::group_by(Sex, Etiology) %>%
  dplyr::summarize(VSA_mean = mean(VSA_b, na.rm =T), VSA_sd = sd(VSA_b, na.rm = T),
                   Disp_mean = mean(vowel_ED_b, na.rm =T), Disp_sd = sd(vowel_ED_b, na.rm =T),
                   Hull_mean = mean(Hull_b, na.rm =T), Hull_sd = sd(Hull_b, na.rm =T),
                   VSD25_mean = mean(Hull_bVSD_25, na.rm =T), VSD25_sd = sd(Hull_bVSD_25, na.rm =T),
                   VSD50_mean = mean(Hull_bVSD_50, na.rm =T), VSD50_sd = sd(Hull_bVSD_50, na.rm =T),
                   VSD75_mean = mean(Hull_bVSD_75, na.rm =T), VSD75_sd = sd(Hull_bVSD_75, na.rm =T),
                   VAS_mean = mean(VAS, na.rm =T), VAS_sd = sd(VAS, na.rm =T),
                   OT_mean = mean(transAcc, na.rm =T), OT_sd = sd(transAcc, na.rm =T)) %>%
  pivot_longer(cols = VSA_mean:OT_sd, names_to = "Measure",
               values_to = "Value") %>%
  dplyr::mutate(Value = round(Value, digits = 2),
                meanSD = ifelse(grepl("_mean",Measure),"M","sd"),
                Measure = gsub("_mean","",Measure),
                Measure = gsub("_sd","",Measure),
                Etiology = paste(Etiology,meanSD, sep = "_"),
                Sex = case_when(
                  Sex == "All" ~ "All Speakers",
                  Sex == "M" ~ "Male Speakers",
                  Sex == "F" ~ "Female Speakers"
                )) %>%
  dplyr::select(!meanSD) %>%
  pivot_wider(names_from = Etiology, values_from = "Value") %>%
  dplyr::filter(Measure != "VSD50")

`summarise()` has grouped output by 'Sex'. You can override using the `.groups` argument.

gtData %>%
  gt::gt(
    rowname_col = "Measure",
    groupname_col = "Sex",
  ) %>%
  fmt_number(
    columns = 'All Etiologies_M':PD_sd,
    decimals = 2
  ) %>%
  tab_spanner(
    label = "All Etiologies",
    columns = c('All Etiologies_M', 'All Etiologies_sd')
  ) %>%
    tab_spanner(
    label = "ALS",
    columns = c(ALS_M, ALS_sd)
  ) %>%
  tab_spanner(
    label = "PD",
    columns = c(PD_M, PD_sd)
  ) %>%
  tab_spanner(
    label = "HD",
    columns = c(HD_M, HD_sd)
  ) %>%
  tab_spanner(
    label = "Ataxic",
    columns = c(Ataxic_M, Ataxic_sd)
  ) %>%
  gt::cols_move_to_start(
    columns = c('All Etiologies_M','All Etiologies_sd')
  ) %>%
  row_group_order(
    groups = c("All Speakers", "Female Speakers", "Male Speakers")
    ) %>%
  cols_label(
     'All Etiologies_M' = "M",
     'All Etiologies_sd' = "SD",
     ALS_M = "M",
     ALS_sd = "SD",
     PD_M = "M",
     PD_sd = "SD",
     HD_M = "M",
     HD_sd = "SD",
     Ataxic_M = "M",
     Ataxic_sd = "SD"
  ) %>%
  gtsave("DescriptivesTable.html", path = "Tables")

OT Model

sjPlot::tab_model(OT_Model1,
                  OT_Model2,
                  OT_Model3b,
                  OT_Model4,
                  OT_Model5,
                  OT_Model_final,
                  show.ci = F,
                  p.style = "stars",
                  file = "Tables/OT Models.html")

VAS Model

sjPlot::tab_model(VAS_Model1,
                  VAS_Model2,
                  VAS_Model3,
                  VAS_Model4,
                  VAS_Model5,
                  VAS_Model_final,
                  show.ci = F,
                  p.style = "stars",
                  file = "Tables/VAS Models.html")

OT vs. VAS

sjPlot::tab_model(OT_VAS_model,OT_VAS_final,
                  show.ci = F,
                  show.reflvl = TRUE,
                  p.style = "stars",
                  file = "Tables/OT and VAS Comparison.html")

Manuscript Figures

Example Measures

formantColor <- "grey"
formantAlpha <- .95
lineColor <- "white"
lineAlpha <- .8

vowelData <- rio::import("Prepped Data/Vowel Data.csv") %>%
  dplyr::filter(Speaker == "AF8")

  Pitch_PRAAT <- list.files(path = paste("Prepped Data/Example Data/", sep = ""), 
                              pattern = ".Pitch", ignore.case = T) %>%
    paste("Prepped Data/Example Data/",., sep = "") %>%
    read.delim(., header = F) %>%
    dplyr::rename(Pitch = V1) %>%
    dplyr::mutate(Pitch = gsub("--undefined--",NA,Pitch),
                  Pitch = as.numeric(Pitch))
  
  Formants_PRAAT <- list.files(path = paste("Prepped Data/Example Data/", sep = ""), 
                              pattern = "_Formant", ignore.case = T) %>%
    paste("Prepped Data/Example Data/",., sep = "") %>%
    read.delim(., header = T) %>%
    dplyr::select(!c(nformants, B1.Hz., B2.Hz., B3.Hz., F4.Hz., B4.Hz., F5.Hz., B5.Hz.)) %>%
    dplyr::rename(Time_s = time.s.,
                  F1_Hz = F1.Hz.,
                  F2_Hz = F2.Hz.,
                  F3_Hz = F3.Hz.) %>%
    dplyr::mutate(F1_Hz = ifelse(F1_Hz == 0, NA, F1_Hz),
                  F2_Hz = ifelse(F2_Hz == 0, NA, F2_Hz),
                  F3_Hz = ifelse(F3_Hz == 0, NA, F3_Hz)) %>%
    dplyr::mutate(F1_Hz = as.numeric(F1_Hz),
                  F2_Hz = as.numeric(F2_Hz),
                  F3_Hz = suppressWarnings(as.numeric(F3_Hz)),
                  Time_ms = Time_s / 1000,
                  F1_kHz = F1_Hz / 1000,
                  F2_kHz = F2_Hz / 1000,
                  F3_kHz = F3_Hz / 1000) %>%
    dplyr::select(!Time_s) %>%
    dplyr::relocate(Time_ms, .before = F1_Hz) %>%
    cbind(.,Pitch_PRAAT)
  
  c <- 2
  while(c < NROW(Formants_PRAAT)){
    Formants_PRAAT$F1_Hz[c] <- ifelse(is.na(Formants_PRAAT$F1_Hz[c-1]) &&
                                        is.na(Formants_PRAAT$F1_Hz[c+1]),
                                      NA,
                                      Formants_PRAAT$F1_Hz[c])
    Formants_PRAAT$F2_Hz[c] <- ifelse(is.na(Formants_PRAAT$F2_Hz[c-1]) &&
                                        is.na(Formants_PRAAT$F2_Hz[c+1]),
                                      NA,
                                      Formants_PRAAT$F2_Hz[c])
    c <- c + 1
  }
  rm(c)
  
  Formants_PRAAT <- Formants_PRAAT %>%
    dplyr::filter(!is.na(Pitch)) %>%
    dplyr::mutate(F1_mad = (abs(F1_Hz - median(F1_Hz))/ mad(F1_Hz, constant = 1.4826)) > 2.5,
                  F2_mad = (abs(F2_Hz - median(F2_Hz))/ mad(F2_Hz, constant = 1.4826)) > 2.5) %>%
    dplyr::filter(F1_mad == FALSE & F2_mad == FALSE) %>%
    dplyr::mutate(mDist = mahalanobis(cbind(.$F1_Hz, .$F2_Hz),
                                      colMeans(cbind(.$F1_Hz, .$F2_Hz)),
                                      cov = cov(cbind(.$F1_Hz, .$F2_Hz))),
                  mDist_sd = abs(scale(mDist,center = T))) %>%
    dplyr::filter(mDist_sd < 2) %>%
    dplyr::select(!c(F1_mad, F2_mad, mDist, mDist_sd)) %>%
    dplyr::mutate(F1_z = scale(F1_Hz, center = TRUE),
                  F2_z = scale(F2_Hz, center = TRUE),
                  F3_z = scale(F3_Hz, center = TRUE),
                  F1_b = emuR::bark(F1_Hz),
                  F2_b = emuR::bark(F2_Hz),
                  F3_b = emuR::bark(F3_Hz))
  
  rm(Pitch_PRAAT)
  
  
## Corner Dispersion ----
  wedge <- vowelData %>%
    dplyr::group_by(Vowel) %>%
    dplyr::summarize(mean_F1 = mean(F1_tempMid),
              mean_F2 = mean(F2_tempMid),
              mean_F1_z = mean(F1_z_tempMid),
              mean_F2_z = mean(F2_z_tempMid),
              mean_F1_b = mean(F1_b_tempMid),
              mean_F2_b = mean(F2_b_tempMid)) %>%
    dplyr::filter(Vowel == "v")
    
  corner_dis <- vowelData %>%
    dplyr::filter(Vowel != "v") %>%
    dplyr::group_by(Vowel) %>%
    dplyr::summarize(mean_F1 = mean(F1_tempMid),
              mean_F2 = mean(F2_tempMid),
              mean_F1_z = mean(F1_z_tempMid),
              mean_F2_z = mean(F2_z_tempMid),
              mean_F1_b = mean(F1_b_tempMid),
              mean_F2_b = mean(F2_b_tempMid)) %>%
    dplyr::mutate(Vowel_ED = sqrt((mean_F1-wedge$mean_F1)^2 + (mean_F2-wedge$mean_F2)^2),
                  Vowel_ED_z = sqrt((mean_F1_z-wedge$mean_F1_z)^2 + (mean_F2_z-wedge$mean_F2_z)^2),
                  Vowel_ED_b = sqrt((mean_F1_b-wedge$mean_F1_b)^2 + (mean_F2_b-wedge$mean_F2_b)^2))

    
# Plot Corner Dispersion
      # Changing to IPA symbols
      corner_dis <- corner_dis %>%
        dplyr::mutate(Vowel = dplyr::case_when(
          Vowel == "ae" ~ "æ",
          TRUE ~ Vowel
        ))
      
      wedge <- wedge %>%
        dplyr::mutate(Vowel = case_when(
          Vowel == "v" ~ "ʌ",
          TRUE ~ Vowel
        ))
      
      CDplot <- ggplot(aes(x=F2_b,
                           y=F1_b),
                       data = Formants_PRAAT,
                       inherit.aes = FALSE) + 
      geom_point(shape = 21,
                 alpha = formantAlpha,
                 color = formantColor) + 
      geom_line(aes(x = mean_F2_b,
                    y = mean_F1_b),
                data = corner_dis %>%
                  dplyr::select(Vowel:mean_F2_b) %>%
                  dplyr::filter(Vowel == "i") %>%
                  rbind(.,wedge),
                color = lineColor,
                size = 1.5,
                alpha = lineAlpha) +
      geom_line(aes(x = mean_F2_b,
                    y = mean_F1_b),
                data = corner_dis %>%
                  dplyr::select(Vowel:mean_F2_b) %>%
                  dplyr::filter(Vowel == "a") %>%
                  rbind(.,wedge),
                color = lineColor,
                size = 1.5,
                alpha = lineAlpha) +
      geom_line(aes(x = mean_F2_b,
                    y = mean_F1_b),
                data = corner_dis %>%
                  dplyr::select(Vowel:mean_F2_b) %>%
                  dplyr::filter(Vowel == "æ") %>%
                  rbind(.,wedge),
                color = lineColor,
                size = 1.5,
                alpha = lineAlpha) +
      geom_line(aes(x = mean_F2_b,
                    y = mean_F1_b),
                data = corner_dis %>%
                  dplyr::select(Vowel:mean_F2_b) %>%
                  dplyr::filter(Vowel == "u") %>%
                  rbind(.,wedge),
                color = lineColor,
                size = 1.5,
                alpha = lineAlpha) +
      geom_point(aes(x = mean_F2_b,
                     y = mean_F1_b,
                     color = Vowel),
                 data = corner_dis %>%
                  dplyr::select(Vowel:mean_F2_b) %>%
                  rbind(.,wedge),
                 inherit.aes = FALSE,
                 size = 5) +
      scale_y_reverse() +
      scale_x_reverse() +
      theme_classic() + labs(title = paste("Corner Dispersion")) + xlab("F2 (Bark)") + ylab("F1 (Bark)") +
      theme(plot.title = element_text(hjust = 0.5),
            aspect.ratio = 1) +
          scale_color_manual(values = c("a" = "#1AAD77",
                                        "æ" = "#1279B5",
                                        "i" = "#FFBF00",
                                        "u" = "#FD7853",
                                        "ʌ" = "#BF3178"))
    CDplot

    
      rm(corner_dis, wedge)
      
## Vowel Space Area ----
  VSA_coords <- vowelData %>%
    dplyr::filter(Vowel != "v") %>%
    dplyr::group_by(Vowel) %>%
    dplyr::summarize(mean_F1 = mean(F1_tempMid),
              mean_F2 = mean(F2_tempMid),
              mean_F1_z = mean(F1_z_tempMid),
              mean_F2_z = mean(F2_z_tempMid),
              mean_F1_b = mean(F1_b_tempMid),
              mean_F2_b = mean(F2_b_tempMid)) 
  
### Plotting VSA
    VSA_coords <- VSA_coords %>%
        dplyr::mutate(Vowel = case_when(
          Vowel == "ae" ~ "æ",
          TRUE ~ Vowel
        ))
    
    VSAplot <- ggplot(aes(x = F2_b,
                          y = F1_b),
                      data = Formants_PRAAT,
                      inherit.aes = FALSE) + 
      geom_point(shape = 21,
                 alpha = formantAlpha,
                 color = formantColor) + 
      geom_polygon(aes(x = mean_F2_b,
                       y = mean_F1_b),
                   data = VSA_coords,
                   alpha = lineAlpha,
                   color = lineColor,
                   fill=NA,
                   size = 1.5) +
      geom_point(aes(x = mean_F2_b,
                     y = mean_F1_b,
                     color = Vowel),
                 data = VSA_coords,
                 inherit.aes = FALSE,
                 size = 5) +
      scale_y_reverse() +
      scale_x_reverse() +
      guides(color = FALSE) +
      theme_classic() + labs(title = "VSA") + xlab("F2 (Bark)") + ylab("F1 (Bark)") +
      theme(plot.title = element_text(hjust = 0.5),
            aspect.ratio = 1) +
                scale_color_manual(values = c("a" = "#1AAD77",
                                        "æ" = "#1279B5",
                                        "i" = "#FFBF00",
                                        "u" = "#FD7853"))

`guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> = "none")` instead.

    VSAplot

  
  rm(VSA_coords)
  
## Hull ----
### Plotting Hull
      convexCoords <- Formants_PRAAT %>%
        dplyr::select(F1_b, F2_b) %>%
        as.matrix() %>%
        grDevices::chull()
      convex <- Formants_PRAAT %>%
        slice(convexCoords)

      hullPlot <- ggplot(aes(F2_b, F1_b),
                         data = Formants_PRAAT) +
        geom_point(shape = 21,
                 alpha = formantAlpha,
                 color = formantColor) +
        geom_polygon(data = convex,
                     alpha = .5,
                     color = "#1279B5",
                     fill = NA,
                     size = 1.5) +
        scale_y_reverse() +
        scale_x_reverse() +
        theme_classic() + labs(title = expression("VSA"[Hull])) +
                                 xlab("F2 (Bark)") +
                                 ylab("F1 (Bark)") +
      theme(plot.title = element_text(hjust = 0.5),
            aspect.ratio = 1)
      hullPlot

    
  
## Vowel Space Density ----

# Bark Normalized Density ----
# selecting the bandwidth
H_hpi <- ks::Hpi(x = Formants_PRAAT[,c("F2_b","F1_b")], pilot = "samse", pre = "scale", binned = T)

# compute 2d kde
k <- kde(x = Formants_PRAAT[,c("F2_b","F1_b")],
         H = H_hpi,
         binned = T,
         gridsize = 250)

#density <- k[["estimate"]]

# Before we can plot the density estimate we need to melt it into long format
mat.melted <- data.table::melt(k$estimate)

The melt generic in data.table has been passed a matrix and will attempt to redirect to the relevant reshape2 method; please note that reshape2 is deprecated, and this redirection is now deprecated as well. To continue using melt methods from reshape2 while both libraries are attached, e.g. melt.list, you can prepend the namespace like reshape2::melt(k$estimate). In the next version, this warning will become an error.

names(mat.melted) <- c("x", "y", "density")

# We need to add two more colums to preserve the axes units
mat.melted$F2.b <- rep(k$eval.points[[1]], times = nrow(k$estimate))
mat.melted$F1.b <- rep(k$eval.points[[2]], each = nrow(k$estimate))
mat.melted$density <- scales::rescale(mat.melted$density, to = c(0, 1))

# VSD - 25
nVSD_25 <- mat.melted %>%
  dplyr::filter(density > .25) %>%
  dplyr::select(F2.b,F1.b, density) %>%
  dplyr::rename(Density = density)

convexCoords <- nVSD_25 %>%
  dplyr::select(F2.b, F1.b) %>%
  as.matrix() %>%
  #grDevices::xy.coords() %>%
  grDevices::chull()
nconvex_25 <- nVSD_25 %>%
  slice(convexCoords)

# VSD - 75
nVSD_75 <- mat.melted %>%
  dplyr::filter(density > .75) %>%
  dplyr::select(F2.b,F1.b, density) %>%
  dplyr::rename(Density = density)

convexCoords <- nVSD_75 %>%
  dplyr::select(F2.b, F1.b) %>%
  as.matrix() %>%
  grDevices::chull()
nconvex_75 <- nVSD_75 %>%
  slice(convexCoords)

# Plotting Z Normalized VSD 
    rf <- colorRampPalette(rev(RColorBrewer::brewer.pal(11, "Spectral")))
    r <- rf(32)
    
    plotData <- mat.melted %>%
                        dplyr::rename(Density = density) %>%
                        dplyr::mutate(VSDlabel = dplyr::case_when(
                          Density < .25 ~ "none",
                          Density > .25 && Density < .75 ~ "VSD25",
                          TRUE ~ "VSD75"
                        ))
geom.text.size <- 2
    VSDplot <- ggplot(data = plotData,
                      aes(x = F2.b,
                          y = F1.b,
                          fill = Density)) + 
      geom_tile() + 
      scale_fill_viridis_c() +
      scale_x_reverse(expand = c(0, 0), 
                      breaks = round(seq(min(mat.melted$F2.b), 
                                         max(mat.melted$F2.b), by = 2))) +
      scale_y_reverse(expand = c(0, 0),
                      breaks = round(seq(min(mat.melted$F1.b),
                                         max(mat.melted$F1.b), by = 2))) + 
      ylab("F1 (Bark)") + xlab("F2 (Bark)") +
      labs(title = "VSD") +
      theme(plot.title = element_text(hjust = 0.5),
            aspect.ratio = 1) +
      geom_polygon(data = nconvex_25, alpha = lineAlpha, color = lineColor, size = 1.5, fill = NA, linetype = 2) +
      geom_polygon(data = nconvex_75, alpha = lineAlpha, color = lineColor, size = 1.5, fill = NA, linetype = 1) +
    # VSD 25 Label
      annotate(geom = "curve",
               x = 6.9, y = 1.7+.5,
               xend = 8.5, yend = 3.5,
               curvature = -.3,
               arrow = arrow(length = unit(2, "mm")),
               color = "white") +
      annotate(geom = "text",
               x = 7.5, y = 1.7,
               label = deparse(bquote(VSD[25])),
               hjust = "center",
               color = "white",
               parse=TRUE) +
    # VSD 75 Label
      annotate(geom = "curve",
               x = 7.5, y = 7.5-.5,
               xend = 11.35, yend = 5.5,
               curvature = .3,
               arrow = arrow(length = unit(2, "mm")),
               color = "white") +
      annotate(geom = "text",
               x = 7, y = 7.5,
               label = deparse(bquote(VSD[75])),
               hjust = "center",
               color = "white",
               parse = TRUE)
     VSDplot

# Combined Plot
     
     row1 <- VSAplot + CDplot +
        patchwork::plot_layout(guides = 'collect',
                         ncol = 2) & theme(legend.position = 'right')
     row2 <- hullPlot + VSDplot +
        patchwork::plot_layout(guides = 'collect',
                         ncol = 2) & theme(legend.position = 'right')
     
     measuresPlot <- row1 / row2 + patchwork::plot_layout(heights = c(1/2, 1/2), byrow = FALSE)
     measuresPlot
     
     rm(row1, row2)

ggsave(filename = "Plots/Measures.png",
       plot = measuresPlot,
       height = 5.5,
       width = 6,
       scale = .8)

Filtering Process

formantAlpha <- .20
myPal <- c("#1279B5","#2D2D37")

Pitch_PRAAT <- list.files(path = paste("Prepped Data/Example Data/", sep = ""), 
                              pattern = ".Pitch", ignore.case = T) %>%
    paste("Prepped Data/Example Data/",., sep = "") %>%
    read.delim(., header = F) %>%
    dplyr::rename(Pitch = V1) %>%
    dplyr::mutate(Pitch = gsub("--undefined--",NA,Pitch),
                  Pitch = as.numeric(Pitch))

Formants_PRAAT <- list.files(path = paste("Prepped Data/Example Data/", sep = ""), 
                              pattern = "_Formant", ignore.case = T) %>%
    paste("Prepped Data/Example Data/",., sep = "") %>%
    read.delim(., header = T) %>%
    dplyr::select(!c(nformants, B1.Hz., B2.Hz., B3.Hz., F4.Hz., B4.Hz., F5.Hz., B5.Hz.)) %>%
    dplyr::rename(Time_s = time.s.,
                  F1_Hz = F1.Hz.,
                  F2_Hz = F2.Hz.,
                  F3_Hz = F3.Hz.) %>%
    dplyr::mutate(F1_Hz = ifelse(F1_Hz == 0, NA, F1_Hz),
                  F2_Hz = ifelse(F2_Hz == 0, NA, F2_Hz),
                  F3_Hz = ifelse(F3_Hz == 0, NA, F3_Hz)) %>%
    dplyr::mutate(F1_Hz = as.numeric(F1_Hz),
                  F2_Hz = as.numeric(F2_Hz),
                  F3_Hz = suppressWarnings(as.numeric(F3_Hz)),
                  Time_ms = Time_s / 1000,
                  F1_kHz = F1_Hz / 1000,
                  F2_kHz = F2_Hz / 1000,
                  F3_kHz = F3_Hz / 1000,
                  F1_b = emuR::bark(F1_Hz),
                  F2_b = emuR::bark(F2_Hz),
                  F3_b = emuR::bark(F3_Hz)) %>%
    dplyr::select(!Time_s) %>%
    dplyr::relocate(Time_ms, .before = F1_Hz) %>%
    cbind(.,Pitch_PRAAT)
  
  c <- 2
  while(c < NROW(Formants_PRAAT)){
    Formants_PRAAT$F1_Hz[c] <- ifelse(is.na(Formants_PRAAT$F1_Hz[c-1]) &&
                                        is.na(Formants_PRAAT$F1_Hz[c+1]),
                                      NA,
                                      Formants_PRAAT$F1_Hz[c])
    Formants_PRAAT$F2_Hz[c] <- ifelse(is.na(Formants_PRAAT$F2_Hz[c-1]) &&
                                        is.na(Formants_PRAAT$F2_Hz[c+1]),
                                      NA,
                                      Formants_PRAAT$F2_Hz[c])
    c <- c + 1
  }
  rm(c)
  
  # Raw Formants ----
  f1 <- ggplot(aes(x=F2_b,
                   y=F1_b),
               data = Formants_PRAAT) + 
      geom_point(shape = 21, color = myPal[2]) +
      scale_y_reverse(limits = c(16,0)) +
      scale_x_reverse(limits = c(19,3)) +
      scale_color_manual(values = myPal) +
      theme_classic() + labs(title = paste("Raw Formant\nValues")) + xlab("F2 (Bark)") + ylab("F1 (Bark)") +
      theme(plot.title = element_text(hjust = 0.5),
            aspect.ratio = 1,
            legend.title = element_blank(),
            legend.text = element_text(size=12))
    
# Step #1: Voiced Segments ----
    plotData <- Formants_PRAAT %>%
                   dplyr::mutate(isOutlier = case_when(
                     is.na(Pitch) ~ "Removed",
                     TRUE ~ "Retained"
                   ))
    f2 <- ggplot(data = plotData,
                 aes(x = F2_b,
                     y = F1_b,
                     color = isOutlier)) + 
      geom_point(shape = 21, data = plotData %>%
                   dplyr::filter(isOutlier == "Removed")) +
      geom_point(shape = 21, data = plotData %>%
             dplyr::filter(isOutlier == "Retained")) +
      scale_y_reverse(limits = c(16,0)) +
      scale_x_reverse(limits = c(19,3)) +
      scale_color_manual(values = myPal) +
      theme_classic() + labs(title = paste("Voiced Segments")) +
      xlab("F2 (Bark)") +
      ylab("F1 (Bark)") +
      theme(plot.title = element_text(hjust = 0.5),
            aspect.ratio = 1,
            legend.title = element_blank(),
            legend.text = element_text(size=12))
    
# Step 2: MAD ----
    plotData <- Formants_PRAAT %>%
      dplyr::filter(!is.na(Pitch)) %>%
      dplyr::mutate(F1_mad = (abs(F1_Hz - median(F1_Hz))/ mad(F1_Hz, constant = 1.4826)) > 2.5,
                    F2_mad = (abs(F2_Hz - median(F2_Hz))/ mad(F2_Hz, constant = 1.4826)) > 2.5,
                    isOutlier = case_when(
                      F1_mad == TRUE | F2_mad == TRUE ~ "Removed",
                      TRUE ~ "Retained"
               ))
    
    f3 <- ggplot(data = plotData,
                 aes(x = F2_b,
                     y = F1_b,
                     color = isOutlier)) + 
      geom_point(shape = 21, data = plotData %>%
                   dplyr::filter(isOutlier == "Removed")) +
      geom_point(shape = 21, data = plotData %>%
             dplyr::filter(isOutlier == "Retained")) +
      scale_y_reverse(limits = c(16,0)) +
      scale_x_reverse(limits = c(19,3)) +
      scale_color_manual(values = myPal) +
      theme_classic() +
      labs(title = paste("Median Absolute\nDeviation")) +
      xlab("F2 (Bark)") +
      ylab("F1 (Bark)") +
      theme(plot.title = element_text(hjust = 0.5),
            aspect.ratio = 1,
            legend.title = element_blank(),
            legend.text = element_text(size=12))
    
# Step 3: Mahalanhobis Distance ----
  plotData <- Formants_PRAAT %>%
      dplyr::filter(!is.na(Pitch)) %>%
      dplyr::mutate(F1_mad = (abs(F1_Hz - median(F1_Hz))/ mad(F1_Hz, constant = 1.4826)) > 2.5,
                    F2_mad = (abs(F2_Hz - median(F2_Hz))/ mad(F2_Hz, constant = 1.4826)) > 2.5) %>%
      dplyr::filter(F1_mad == FALSE & F2_mad == FALSE) %>%
      dplyr::mutate(mDist = mahalanobis(cbind(.$F1_Hz, .$F2_Hz),
                                      colMeans(cbind(.$F1_Hz, .$F2_Hz)),
                                      cov = cov(cbind(.$F1_Hz, .$F2_Hz))),
                  mDist_sd = abs(scale(mDist,center = T)),
                  isOutlier = case_when(
                    mDist_sd < 2 ~ "Retained",
                    TRUE ~ "Removed"
                  ))
    
    f4 <- ggplot(data = plotData,
                 aes(x = F2_b,
                     y = F1_b,
                     color = isOutlier)) + 
      geom_point(shape = 21, data = plotData %>%
                   dplyr::filter(isOutlier == "Removed")) +
      geom_point(shape = 21, data = plotData %>%
             dplyr::filter(isOutlier == "Retained")) +
      scale_y_reverse(limits = c(16,0)) +
      scale_x_reverse(limits = c(19,3)) +
      scale_color_manual(values = myPal) +
      theme_classic() + labs(title = paste("Mahalanobis\nDistance")) + xlab("F2 (Bark)") + ylab("F1 (Bark)") +
      theme(plot.title = element_text(hjust = 0.5),
            aspect.ratio = 1,
            legend.title = element_blank(),
            legend.text = element_text(size=12))
    
# Final Formants ----
    plotData <- Formants_PRAAT %>%
    dplyr::filter(!is.na(Pitch)) %>%
    dplyr::mutate(F1_mad = (abs(F1_Hz - median(F1_Hz))/ mad(F1_Hz, constant = 1.4826)) > 2.5,
                  F2_mad = (abs(F2_Hz - median(F2_Hz))/ mad(F2_Hz, constant = 1.4826)) > 2.5) %>%
    dplyr::filter(F1_mad == FALSE & F2_mad == FALSE) %>%
    dplyr::mutate(mDist = mahalanobis(cbind(.$F1_Hz, .$F2_Hz),
                                      colMeans(cbind(.$F1_Hz, .$F2_Hz)),
                                      cov = cov(cbind(.$F1_Hz, .$F2_Hz))),
                  mDist_sd = abs(scale(mDist,center = T))) %>%
    dplyr::filter(mDist_sd < 2)
    
    f5 <- ggplot(aes(x=F2_b,
                     y=F1_b),
                       data = plotData,
                       inherit.aes = FALSE) + 
      geom_point(shape = 21, color = myPal[2]) + 
      scale_y_reverse(limits = c(16,0)) +
      scale_x_reverse(limits = c(19,3)) +
      theme_classic() + labs(title = paste("Final Formant\nTrajectories")) + xlab("F2 (Bark)") + ylab("F1 (Bark)") +
      theme(plot.title = element_text(hjust = 0.5),
            aspect.ratio = 1, legend.title = element_blank())
    
# Comibing plots
    filteredPlot <- f1 + f2 + f3 + f4 + f5 + patchwork::guide_area() +
      patchwork::plot_layout(guides = 'collect',
                         ncol = 3) +
      patchwork::plot_annotation(tag_levels = 'A')
    filteredPlot
    
    ggsave(plot = filteredPlot, "Plots/Filtered Formants.png",
           height = 6,
           width = 8,
           units = "in",
           scale = .9)

NA

OT vs. VAS

plotData_Int <- AcousticData %>%
  dplyr::filter(!grepl("_rel", Speaker)) %>%
  dplyr::group_by(Speaker) %>%
  dplyr::mutate(segMin = base::min(VAS, transAcc),
                segMax = base::max(VAS, transAcc),
                ratingAvg = mean(VAS, transAcc, na.rm = T),
                Speaker = as.factor(Speaker),
                Etiology = case_when(
                  Etiology == "Ataxic" ~ "Ataxia",
                  TRUE ~ as.character(Etiology)
                ),
                Etiology = as.factor(Etiology)) %>%
  arrange(segMax)

my_pal <- c("#f26430", "#272D2D","#256eff")
# With a bit more style
plot_Int <- ggplot(plotData_Int) +
  geom_segment(aes(x = fct_inorder(Speaker),
                   xend = Speaker,
                   y = segMin,
                   yend = segMax,
                   color = Etiology)) +
  geom_point(aes(x = Speaker,
                 y = VAS,
                 color = Etiology),
             #color = my_pal[1],
             size = 3,
             shape = 19) +
  geom_point(aes(x = Speaker,
                 y = transAcc,
                 color = Etiology),
             #color = my_pal[2],
             size = 3,
             shape = 15) +
  coord_flip()+
  theme_classic() +
  theme(
    legend.position = "none",
    panel.border = element_blank(),
  ) +
  xlab("") +
  ylab("Speech Intelligibility") +
  ggtitle("Speech Intelligibility") +
  ylim(c(0,100))


myPal <- c("#1AAD77", "#1279B5", "#FFBF00", "#FD7853", "#BF3178")
myShapes <- c(16, 18, 17, 15)

OT_VASscatter <- ggplot(plotData_Int,
                  aes(x = VAS,
                      y = transAcc,
                      color = Etiology,
                      shape = Etiology,
                      linetype = Etiology)) +
  geom_point() +
  geom_smooth(method = "lm", se = F) +
  geom_abline(intercept = 0, slope = 1) +
  coord_cartesian(xlim = c(0,100), ylim = c(0,100)) +
  labs(x = "Intelligibility (VAS)", y = "Intelligibility (OT)") +
  scale_color_manual(values = myPal) +
  scale_shape_manual(values = myShapes) +
  theme_classic() +
  theme(aspect.ratio=1,
        legend.position="right")


ggsave(filename = "Plots/OT and VAS Scatterplot.png",
       plot = OT_VASscatter,
       height = 3.25,
       width = 4,
       units = "in",
       scale = 1)

rm(scatter1, scatter2, combinedScatter)

Model Scatterplots

modelFigureData <- AcousticData %>%
  dplyr::filter(!grepl("_rel",Speaker)) %>%
  dplyr::select(Speaker, Etiology, Sex, VSA_b, vowel_ED_b, Hull_b, Hull_bVSD_25, Hull_bVSD_75, VAS, transAcc) %>%
  dplyr::mutate(Speaker = as.factor(Speaker),
                Etiology = as.factor(Etiology),
                Sex = as.factor(Sex)) %>%
  tidyr::pivot_longer(cols = VAS:transAcc, names_to = "IntType", values_to = "Int") %>%
  dplyr::mutate(IntType = case_when(
    IntType == "transAcc" ~ "OT",
    TRUE ~ "VAS"
  ),
                IntType = as.factor(IntType))

ylabel <- "Intelligibility"
myPal <- c("#2D2D37", "#1279B5")
myPalShape <- c(19, 1)

VSA <- modelFigureData %>%
  ggplot() +
  aes(x = VSA_b,
      y = Int,
      color = IntType,
      shape = IntType,
      linetype = IntType) +
  geom_point() +
  geom_smooth(method = "lm", se = T, fill = "light grey") +
  geom_smooth(method = "lm", se = F) +
  xlab(expression("VSA (Bark"^2*")")) +
  ylab(ylabel) +
  coord_cartesian(ylim = c(0,100)) +
  theme_classic() +
  theme(plot.title = element_text(hjust = 0.5, face = "bold"),
        aspect.ratio=1) +
  scale_color_manual(values = myPal) +
  scale_shape_manual(values = myPalShape) +
  labs(color="Intelligibility Type",
       shape = "Intelligibility Type",
       linetype = "Intelligibility Type")

disp <- modelFigureData %>%
  ggplot() +
  aes(x = vowel_ED_b,
      y = Int,
      color = IntType,
      shape = IntType,
      linetype = IntType) +
  geom_point() +
  geom_smooth(method = "lm", se = T, fill = "light grey") +
  geom_smooth(method = "lm", se = F) +
  xlab("Corner Dispersion (Bark)") +
  ylab(ylabel) +
  coord_cartesian(ylim = c(0,100)) +
  theme_classic() +
  theme(aspect.ratio=1) +
  scale_color_manual(values = myPal) +
  scale_shape_manual(values = myPalShape) +
  labs(color="Intelligibility Type",
       shape = "Intelligibility Type",
       linetype = "Intelligibility Type")

Hull <- modelFigureData %>%
  ggplot() +
  aes(x = Hull_b,
      y = Int,
      color = IntType,
      shape = IntType,
      linetype = IntType) +
  geom_point() +
  geom_smooth(method = "lm", se = T, fill = "light grey") +
  geom_smooth(method = "lm", se = F) +
  xlab(expression("VSA"[Hull]*" (Bark"^2*")")) +
  ylab(ylabel) +
  coord_cartesian(ylim = c(0,100)) +
  theme_classic() +
  theme(aspect.ratio=1) + theme(legend.position = "none") +
  scale_color_manual(values = myPal) +
  scale_shape_manual(values = myPalShape) +
  labs(color="Intelligibility Type",
       shape = "Intelligibility Type",
       linetype = "Intelligibility Type")

vsd25 <- modelFigureData %>%
  ggplot() +
  aes(x = Hull_bVSD_25,
      y = Int,
      color = IntType,
      shape = IntType,
      linetype = IntType) +
  geom_point() +
  geom_smooth(method = "lm", se = T, fill = "light grey") +
  geom_smooth(method = "lm", se = F) +
  xlab(expression("VSD"[25]*" (Bark"^2*")")) +
  ylab(ylabel) +
  coord_cartesian(ylim = c(0,100)) +
  theme_classic() +
  theme(aspect.ratio=1) + theme(legend.position = "none") +
  scale_color_manual(values = myPal) +
  scale_shape_manual(values = myPalShape) +
  labs(color="Intelligibility Type",
       shape = "Intelligibility Type",
       linetype = "Intelligibility Type")

vsd75 <- modelFigureData %>%
  ggplot() +
  aes(x = Hull_bVSD_75,
      y = Int,
      color = IntType,
      shape = IntType,
      linetype = IntType) +
  geom_point() +
  geom_smooth(method = "lm", se = T, fill = "light grey") +
  geom_smooth(method = "lm", se = F) +
  xlab(expression("VSD"[75]*" (Bark"^2*")")) +
  ylab(ylabel) +
  coord_cartesian(ylim = c(0,100)) +
  theme_classic() +
  theme(aspect.ratio=1) + theme(legend.position = "none") +
  scale_color_manual(values = myPal) +
  scale_shape_manual(values = myPalShape) +
  labs(color="Intelligibility Type",
       shape = "Intelligibility Type",
       linetype = "Intelligibility Type")

# Creating OT Scatterplot Figure

scatter <- VSA  + disp + patchwork::guide_area() + Hull + vsd25 + vsd75 +
  patchwork::plot_layout(guides = 'collect',
                         ncol = 3) & theme(legend.position = "right")

scatter 

ggsave("Plots/ModelFigure.png", scatter,
       height = 4,
       width = 6,
       units = "in",
       scale = 1.1)

Filtering at Different Levels

text_x <- 12.5
text_y <- 8.5
xlims <- c(16,5)
ylims <- c(9,1)
# Hull - 2 SD ----
Pitch_PRAAT <- list.files(path = paste("Prepped Data/Example Data", sep = ""), 
                              pattern = ".Pitch", ignore.case = T) %>%
    paste("Prepped Data/Example Data/",., sep = "") %>%
    read.delim(., header = F) %>%
    dplyr::rename(Pitch = V1) %>%
    dplyr::mutate(Pitch = gsub("--undefined--",NA,Pitch),
                  Pitch = as.numeric(Pitch))

Formants_PRAAT <- list.files(path = paste("Prepped Data/Example Data", sep = ""), 
                              pattern = "_Formant", ignore.case = T) %>%
    paste("Prepped Data/Example Data/",., sep = "") %>%
    read.delim(., header = T) %>%
    dplyr::select(!c(nformants, B1.Hz., B2.Hz., B3.Hz., F4.Hz., B4.Hz., F5.Hz., B5.Hz.)) %>%
    dplyr::rename(Time_s = time.s.,
                  F1_Hz = F1.Hz.,
                  F2_Hz = F2.Hz.,
                  F3_Hz = F3.Hz.) %>%
    dplyr::mutate(F1_Hz = ifelse(F1_Hz == 0, NA, F1_Hz),
                  F2_Hz = ifelse(F2_Hz == 0, NA, F2_Hz),
                  F3_Hz = ifelse(F3_Hz == 0, NA, F3_Hz)) %>%
    dplyr::mutate(F1_Hz = as.numeric(F1_Hz),
                  F2_Hz = as.numeric(F2_Hz),
                  F3_Hz = suppressWarnings(as.numeric(F3_Hz)),
                  Time_ms = Time_s / 1000,
                  F1_kHz = F1_Hz / 1000,
                  F2_kHz = F2_Hz / 1000,
                  F3_kHz = F3_Hz / 1000,
                  F1_b = emuR::bark(F1_Hz),
                  F2_b = emuR::bark(F2_Hz),
                  F3_b = emuR::bark(F3_Hz)) %>%
    dplyr::select(!Time_s) %>%
    dplyr::relocate(Time_ms, .before = F1_Hz) %>%
    cbind(.,Pitch_PRAAT) %>%
    dplyr::filter(!is.na(Pitch)) %>%
    dplyr::mutate(F1_mad = (abs(F1_Hz - median(F1_Hz))/ mad(F1_Hz, constant = 1.4826)) > 2.5,
                  F2_mad = (abs(F2_Hz - median(F2_Hz))/ mad(F2_Hz, constant = 1.4826)) > 2.5) %>%
    dplyr::filter(F1_mad == FALSE & F2_mad == FALSE) %>%
    dplyr::mutate(mDist = mahalanobis(cbind(.$F1_Hz, .$F2_Hz),
                                      colMeans(cbind(.$F1_Hz, .$F2_Hz)),
                                      cov = cov(cbind(.$F1_Hz, .$F2_Hz))),
                  mDist_sd = abs(scale(mDist,center = T))) %>%
    #dplyr::mutate(mDistOutlier = (stats::pchisq(mDist, df=1, lower.tail=FALSE)) < .001) %>%
    dplyr::filter(mDist_sd < 2)
  
  c <- 2
  while(c < NROW(Formants_PRAAT)){
    Formants_PRAAT$F1_Hz[c] <- ifelse(is.na(Formants_PRAAT$F1_Hz[c-1]) &&
                                        is.na(Formants_PRAAT$F1_Hz[c+1]),
                                      NA,
                                      Formants_PRAAT$F1_Hz[c])
    Formants_PRAAT$F2_Hz[c] <- ifelse(is.na(Formants_PRAAT$F2_Hz[c-1]) &&
                                        is.na(Formants_PRAAT$F2_Hz[c+1]),
                                      NA,
                                      Formants_PRAAT$F2_Hz[c])
    c <- c + 1
  }
  rm(c)
  
    Hull_b <- cHull(Formants_PRAAT$F1_b, Formants_PRAAT$F2_b)
### Plotting Hull
      convexCoords <- Formants_PRAAT %>%
        dplyr::select(F1_b, F2_b) %>%
        as.matrix() %>%
        grDevices::chull()
      convex <- Formants_PRAAT %>%
        slice(convexCoords)

      hullPlot_2 <- ggplot(aes(F2_b, F1_b),
                         data = Formants_PRAAT) +
        geom_point(shape = 21) +
        geom_polygon(data = convex,
                     alpha = .5,
                     color = "#1279B5",
                     fill = NA,
                     size = 1.5) +
        annotate("text", x = text_x, y = text_y, label = paste("Hull =",round(Hull_b,2))) +
        scale_y_reverse() +
        scale_x_reverse() +
        xlim(xlims) +
        ylim(ylims) +
        theme_classic() + labs(title = paste("2 SD")) + xlab("F2 (Bark)") + ylab("F1 (Bark)") +
        theme(plot.title = element_text(hjust = 0.5),
              aspect.ratio = 1)

Scale for 'x' is already present. Adding another scale for 'x', which will replace the
existing scale.
Scale for 'y' is already present. Adding another scale for 'y', which will replace the
existing scale.

      hullPlot_2

      
# Hull - 2.5 SD ----
Pitch_PRAAT <- list.files(path = paste("Prepped Data/Example Data", sep = ""), 
                              pattern = ".Pitch", ignore.case = T) %>%
    paste("Prepped Data/Example Data/",., sep = "") %>%
    read.delim(., header = F) %>%
    dplyr::rename(Pitch = V1) %>%
    dplyr::mutate(Pitch = gsub("--undefined--",NA,Pitch),
                  Pitch = as.numeric(Pitch))

Formants_PRAAT <- list.files(path = paste("Prepped Data/Example Data", sep = ""), 
                              pattern = "_Formant", ignore.case = T) %>%
    paste("Prepped Data/Example Data/",., sep = "") %>%
    read.delim(., header = T) %>%
    dplyr::select(!c(nformants, B1.Hz., B2.Hz., B3.Hz., F4.Hz., B4.Hz., F5.Hz., B5.Hz.)) %>%
    dplyr::rename(Time_s = time.s.,
                  F1_Hz = F1.Hz.,
                  F2_Hz = F2.Hz.,
                  F3_Hz = F3.Hz.) %>%
    dplyr::mutate(F1_Hz = ifelse(F1_Hz == 0, NA, F1_Hz),
                  F2_Hz = ifelse(F2_Hz == 0, NA, F2_Hz),
                  F3_Hz = ifelse(F3_Hz == 0, NA, F3_Hz)) %>%
    dplyr::mutate(F1_Hz = as.numeric(F1_Hz),
                  F2_Hz = as.numeric(F2_Hz),
                  F3_Hz = suppressWarnings(as.numeric(F3_Hz)),
                  Time_ms = Time_s / 1000,
                  F1_kHz = F1_Hz / 1000,
                  F2_kHz = F2_Hz / 1000,
                  F3_kHz = F3_Hz / 1000,
                  F1_b = emuR::bark(F1_Hz),
                  F2_b = emuR::bark(F2_Hz),
                  F3_b = emuR::bark(F3_Hz)) %>%
    dplyr::select(!Time_s) %>%
    dplyr::relocate(Time_ms, .before = F1_Hz) %>%
    cbind(.,Pitch_PRAAT) %>%
    dplyr::filter(!is.na(Pitch)) %>%
    dplyr::mutate(F1_mad = (abs(F1_Hz - median(F1_Hz))/ mad(F1_Hz, constant = 1.4826)) > 2.5,
                  F2_mad = (abs(F2_Hz - median(F2_Hz))/ mad(F2_Hz, constant = 1.4826)) > 2.5) %>%
    dplyr::filter(F1_mad == FALSE & F2_mad == FALSE) %>%
    dplyr::mutate(mDist = mahalanobis(cbind(.$F1_Hz, .$F2_Hz),
                                      colMeans(cbind(.$F1_Hz, .$F2_Hz)),
                                      cov = cov(cbind(.$F1_Hz, .$F2_Hz))),
                  mDist_sd = abs(scale(mDist,center = T))) %>%
    #dplyr::mutate(mDistOutlier = (stats::pchisq(mDist, df=1, lower.tail=FALSE)) < .001) %>%
    dplyr::filter(mDist_sd < 2.5)
  
  c <- 2
  while(c < NROW(Formants_PRAAT)){
    Formants_PRAAT$F1_Hz[c] <- ifelse(is.na(Formants_PRAAT$F1_Hz[c-1]) &&
                                        is.na(Formants_PRAAT$F1_Hz[c+1]),
                                      NA,
                                      Formants_PRAAT$F1_Hz[c])
    Formants_PRAAT$F2_Hz[c] <- ifelse(is.na(Formants_PRAAT$F2_Hz[c-1]) &&
                                        is.na(Formants_PRAAT$F2_Hz[c+1]),
                                      NA,
                                      Formants_PRAAT$F2_Hz[c])
    c <- c + 1
  }
  rm(c)
  
    Hull_b <- cHull(Formants_PRAAT$F1_b, Formants_PRAAT$F2_b)
### Plotting Hull
      convexCoords <- Formants_PRAAT %>%
        dplyr::select(F1_b, F2_b) %>%
        as.matrix() %>%
        grDevices::chull()
      convex <- Formants_PRAAT %>%
        slice(convexCoords)

      hullPlot_2.5 <- ggplot(aes(F2_b, F1_b),
                         data = Formants_PRAAT) +
        geom_point(shape = 21) +
        geom_polygon(data = convex,
                     alpha = .5,
                     color = "#1279B5",
                     fill = NA,
                     size = 1.5) +
        annotate("text", x = text_x, y = text_y, label = paste("Hull =",round(Hull_b,2))) +
        scale_y_reverse() +
        scale_x_reverse() +
        xlim(xlims) +
        ylim(ylims) +
        theme_classic() + labs(title = paste("2.5 SD")) + xlab("F2 (Bark)") + ylab("F1 (Bark)") +
        theme(plot.title = element_text(hjust = 0.5),
              aspect.ratio = 1)

Scale for 'x' is already present. Adding another scale for 'x', which will replace the
existing scale.
Scale for 'y' is already present. Adding another scale for 'y', which will replace the
existing scale.

      hullPlot_2.5

      
# Hull - 3 SD ----
Pitch_PRAAT <- list.files(path = paste("Prepped Data/Example Data", sep = ""), 
                              pattern = ".Pitch", ignore.case = T) %>%
    paste("Prepped Data/Example Data/",., sep = "") %>%
    read.delim(., header = F) %>%
    dplyr::rename(Pitch = V1) %>%
    dplyr::mutate(Pitch = gsub("--undefined--",NA,Pitch),
                  Pitch = as.numeric(Pitch))

Formants_PRAAT <- list.files(path = paste("Prepped Data/Example Data", sep = ""), 
                              pattern = "_Formant", ignore.case = T) %>%
    paste("Prepped Data/Example Data/",., sep = "") %>%
    read.delim(., header = T) %>%
    dplyr::select(!c(nformants, B1.Hz., B2.Hz., B3.Hz., F4.Hz., B4.Hz., F5.Hz., B5.Hz.)) %>%
    dplyr::rename(Time_s = time.s.,
                  F1_Hz = F1.Hz.,
                  F2_Hz = F2.Hz.,
                  F3_Hz = F3.Hz.) %>%
    dplyr::mutate(F1_Hz = ifelse(F1_Hz == 0, NA, F1_Hz),
                  F2_Hz = ifelse(F2_Hz == 0, NA, F2_Hz),
                  F3_Hz = ifelse(F3_Hz == 0, NA, F3_Hz)) %>%
    dplyr::mutate(F1_Hz = as.numeric(F1_Hz),
                  F2_Hz = as.numeric(F2_Hz),
                  F3_Hz = suppressWarnings(as.numeric(F3_Hz)),
                  Time_ms = Time_s / 1000,
                  F1_kHz = F1_Hz / 1000,
                  F2_kHz = F2_Hz / 1000,
                  F3_kHz = F3_Hz / 1000,
                  F1_b = emuR::bark(F1_Hz),
                  F2_b = emuR::bark(F2_Hz),
                  F3_b = emuR::bark(F3_Hz)) %>%
    dplyr::select(!Time_s) %>%
    dplyr::relocate(Time_ms, .before = F1_Hz) %>%
    cbind(.,Pitch_PRAAT) %>%
    dplyr::filter(!is.na(Pitch)) %>%
    dplyr::mutate(F1_mad = (abs(F1_Hz - median(F1_Hz))/ mad(F1_Hz, constant = 1.4826)) > 2.5,
                  F2_mad = (abs(F2_Hz - median(F2_Hz))/ mad(F2_Hz, constant = 1.4826)) > 2.5) %>%
    dplyr::filter(F1_mad == FALSE & F2_mad == FALSE) %>%
    dplyr::mutate(mDist = mahalanobis(cbind(.$F1_Hz, .$F2_Hz),
                                      colMeans(cbind(.$F1_Hz, .$F2_Hz)),
                                      cov = cov(cbind(.$F1_Hz, .$F2_Hz))),
                  mDist_sd = abs(scale(mDist,center = T))) %>%
    #dplyr::mutate(mDistOutlier = (stats::pchisq(mDist, df=1, lower.tail=FALSE)) < .001) %>%
    dplyr::filter(mDist_sd < 3)
  
  c <- 2
  while(c < NROW(Formants_PRAAT)){
    Formants_PRAAT$F1_Hz[c] <- ifelse(is.na(Formants_PRAAT$F1_Hz[c-1]) &&
                                        is.na(Formants_PRAAT$F1_Hz[c+1]),
                                      NA,
                                      Formants_PRAAT$F1_Hz[c])
    Formants_PRAAT$F2_Hz[c] <- ifelse(is.na(Formants_PRAAT$F2_Hz[c-1]) &&
                                        is.na(Formants_PRAAT$F2_Hz[c+1]),
                                      NA,
                                      Formants_PRAAT$F2_Hz[c])
    c <- c + 1
  }
  rm(c)
  
    Hull_b <- cHull(Formants_PRAAT$F1_b, Formants_PRAAT$F2_b)
### Plotting Hull
      convexCoords <- Formants_PRAAT %>%
        dplyr::select(F1_b, F2_b) %>%
        as.matrix() %>%
        grDevices::chull()
      convex <- Formants_PRAAT %>%
        slice(convexCoords)

      hullPlot_3 <- ggplot(aes(F2_b, F1_b),
                         data = Formants_PRAAT) +
        geom_point(shape = 21) +
        geom_polygon(data = convex,
                     alpha = .5,
                     color = "#1279B5",
                     fill = NA,
                     size = 1.5) +
        annotate("text", x = text_x, y = text_y, label = paste("Hull =",round(Hull_b,2))) +
        scale_y_reverse() +
        scale_x_reverse() +
        xlim(xlims) +
        ylim(ylims) +
        theme_classic() + labs(title = paste("3 SD")) + xlab("F2 (Bark)") + ylab("F1 (Bark)") +
        theme(plot.title = element_text(hjust = 0.5),
              aspect.ratio = 1)

Scale for 'x' is already present. Adding another scale for 'x', which will replace the
existing scale.
Scale for 'y' is already present. Adding another scale for 'y', which will replace the
existing scale.

      hullPlot_3

      
# Hull - 1.5 SD ----
Pitch_PRAAT <- list.files(path = paste("Prepped Data/Example Data", sep = ""), 
                              pattern = ".Pitch", ignore.case = T) %>%
    paste("Prepped Data/Example Data/",., sep = "") %>%
    read.delim(., header = F) %>%
    dplyr::rename(Pitch = V1) %>%
    dplyr::mutate(Pitch = gsub("--undefined--",NA,Pitch),
                  Pitch = as.numeric(Pitch))

Formants_PRAAT <- list.files(path = paste("Prepped Data/Example Data", sep = ""), 
                              pattern = "_Formant", ignore.case = T) %>%
    paste("Prepped Data/Example Data/",., sep = "") %>%
    read.delim(., header = T) %>%
    dplyr::select(!c(nformants, B1.Hz., B2.Hz., B3.Hz., F4.Hz., B4.Hz., F5.Hz., B5.Hz.)) %>%
    dplyr::rename(Time_s = time.s.,
                  F1_Hz = F1.Hz.,
                  F2_Hz = F2.Hz.,
                  F3_Hz = F3.Hz.) %>%
    dplyr::mutate(F1_Hz = ifelse(F1_Hz == 0, NA, F1_Hz),
                  F2_Hz = ifelse(F2_Hz == 0, NA, F2_Hz),
                  F3_Hz = ifelse(F3_Hz == 0, NA, F3_Hz)) %>%
    dplyr::mutate(F1_Hz = as.numeric(F1_Hz),
                  F2_Hz = as.numeric(F2_Hz),
                  F3_Hz = suppressWarnings(as.numeric(F3_Hz)),
                  Time_ms = Time_s / 1000,
                  F1_kHz = F1_Hz / 1000,
                  F2_kHz = F2_Hz / 1000,
                  F3_kHz = F3_Hz / 1000,
                  F1_b = emuR::bark(F1_Hz),
                  F2_b = emuR::bark(F2_Hz),
                  F3_b = emuR::bark(F3_Hz)) %>%
    dplyr::select(!Time_s) %>%
    dplyr::relocate(Time_ms, .before = F1_Hz) %>%
    cbind(.,Pitch_PRAAT) %>%
    dplyr::filter(!is.na(Pitch)) %>%
    dplyr::mutate(F1_mad = (abs(F1_Hz - median(F1_Hz))/ mad(F1_Hz, constant = 1.4826)) > 2.5,
                  F2_mad = (abs(F2_Hz - median(F2_Hz))/ mad(F2_Hz, constant = 1.4826)) > 2.5) %>%
    dplyr::filter(F1_mad == FALSE & F2_mad == FALSE) %>%
    dplyr::mutate(mDist = mahalanobis(cbind(.$F1_Hz, .$F2_Hz),
                                      colMeans(cbind(.$F1_Hz, .$F2_Hz)),
                                      cov = cov(cbind(.$F1_Hz, .$F2_Hz))),
                  mDist_sd = abs(scale(mDist,center = T))) %>%
    #dplyr::mutate(mDistOutlier = (stats::pchisq(mDist, df=1, lower.tail=FALSE)) < .001) %>%
    dplyr::filter(mDist_sd < 1.5)
  
  c <- 2
  while(c < NROW(Formants_PRAAT)){
    Formants_PRAAT$F1_Hz[c] <- ifelse(is.na(Formants_PRAAT$F1_Hz[c-1]) &&
                                        is.na(Formants_PRAAT$F1_Hz[c+1]),
                                      NA,
                                      Formants_PRAAT$F1_Hz[c])
    Formants_PRAAT$F2_Hz[c] <- ifelse(is.na(Formants_PRAAT$F2_Hz[c-1]) &&
                                        is.na(Formants_PRAAT$F2_Hz[c+1]),
                                      NA,
                                      Formants_PRAAT$F2_Hz[c])
    c <- c + 1
  }
  rm(c)
  
    Hull_b <- cHull(Formants_PRAAT$F1_b, Formants_PRAAT$F2_b)
### Plotting Hull
      convexCoords <- Formants_PRAAT %>%
        dplyr::select(F1_b, F2_b) %>%
        as.matrix() %>%
        grDevices::chull()
      convex <- Formants_PRAAT %>%
        slice(convexCoords)

      hullPlot_1.5 <- ggplot(aes(F2_b, F1_b),
                         data = Formants_PRAAT) +
        geom_point(shape = 21) +
        geom_polygon(data = convex,
                     alpha = .5,
                     color = "#1279B5",
                     fill = NA,
                     size = 1.5) +
        annotate("text", x = text_x, y = text_y, label = paste("Hull =",round(Hull_b,2))) +
        scale_y_reverse() +
        scale_x_reverse() +
        xlim(xlims) +
        ylim(ylims) +
        theme_classic() + labs(title = paste("1.5 SD")) + xlab("F2 (Bark)") + ylab("F1 (Bark)") +
        theme(plot.title = element_text(hjust = 0.5),
              aspect.ratio = 1)

Scale for 'x' is already present. Adding another scale for 'x', which will replace the
existing scale.
Scale for 'y' is already present. Adding another scale for 'y', which will replace the
existing scale.

      hullPlot_1.5

      
# Combined ----
      ggpubr::ggarrange(hullPlot_1.5, hullPlot_2, hullPlot_2.5, hullPlot_3,
                        ncol = 4)
      ggsave(filename = "Plots/Hull at Different Filters.png",
             height = 3,
             width= 9,
             units = "in",
             scale = 1)

NA

Listener Demographic Information

ListenerDemo <- Listeners %>%
  furniture::table1(age, gender, race, ethnicity)

ListenerDemo

Speaker Demographics

SpeakerDemo <- AcousticData %>%
  dplyr::select(c(Speaker, Sex, Etiology))

Ages <- rio::import("Prepped Data/Speaker Ages.xlsx")

SpeakerDemo <- full_join(SpeakerDemo, Ages, by = "Speaker")

SpeakerDemoInfo <- SpeakerDemo %>%
  furniture::table1(Sex, Etiology, Age, na.rm = F)

SpeakerDemoInfo

SpeakerDemo %>%
  dplyr::summarize(mean_age = mean(Age, na.rm = T), age_sd = sd(Age, na.rm = T), age_range = range(Age, na.rm = T))